Fracture mapping in InSAR data using deep learning

Recent studies of the Reykjanes Peninsula in Iceland have shown that Interferometric Synthetic Aperture Radar (InSAR) data can reveal surface movements across new and pre-existing fractures associated with stress changes during volcanic dyke intrusions. These data have revealed many fractures in areas where optical imagery or field observations are obscured by vegetation, infrastructure, or young lava flows. Mapping active faults and fractures in geologically dynamic regions is essential for assessing tectonic and volcanic hazards, as pre-existing fractures and crustal weaknesses can control magma pathways, dyke propagation, and the location of eruptive activity. However, systematic fracture mapping from wrapped interferograms remains a time-consuming manual task. Deep learning approaches have been widely utilized successfully for mapping faults in seismic data and optical images, and similarly, to detect glacier crevasses in SAR backscatter images. Here, we investigate the feasibility of automatic fracture mapping directly from wrapped interferograms using deep learning, focusing on the current volcanic unrest on the Reykjanes Peninsula. We address the task as a binary classification problem, and implement a convolutional neural network with a U-Net architecture trained using a Dice loss to address strong class imbalance. We initially trained our model on a rather small, highly imbalanced dataset consisting of Sentinel-1 and TerraSAR-X interferograms of the area from September 2023 to February 2024. Despite relatively modest F1-Score (~56%), the model successfully identifies all major fracture movements in the test data and is able to detect features absent from the original labels, providing a fairly robust fracture map that can be easily refined. These results demonstrate that deep learning can be used to extract meaningful structural information from wrapped interferograms, even with limited data and imperfect training labels, and constitute to the best our knowledge the first application of deep learning to fracture mapping in wrapped interferograms. Current work is aimed at improving the model performance by including the latest fractures dataset of the Reykjanes Peninsula, consisting of fractures mapped on TerraSAR-X interferograms from September 2021 to July 2024. Additionally, ongoing efforts are focused on generating physically realistic synthetic interferograms that capture the complexity of fracturing and fracture reactivation due to dyke emplacement and propagation and other sources of deformation. By addressing the current limitations, this approach has the potential to enable transferable fracture-mapping workflows applicable across diverse tectonic settings and InSAR datasets, contributing to more efficient geohazard monitoring.